Design, development, and evaluation of docetaxel-loaded niosomes for the treatment of breast cancer

DTX niosomes were prepared by using DTX, Span® 40, PF108, and the mixture of methanol and chloroform by using the thin-layer evaporation method. The technique of thin film hydration was used to prepare niosomes as it produces multilamellar non-ionic niosomal vesicles. Besides, it is the most efficient, simple, and reproducible method. To fabricate niosomes with a narrow size distribution, it is typically combined with sonication [34]. Historically, a variety of non-ionic surfactants have been employed to decrease the particle size and enhance the zeta potential of drug-free niosomal formulations [31]. Cholesterol, as it interacts with non-ionic surfactants, was used in the right proportion to produce the most stable formulation with an improved niosomal mechanical strength as well as water permeability that will retain its integrity under high-stress conditions [35]. Span® 40 is a hydrophobic amphiphile with a HLB value of 6.7, and DTX is also hydrophobic. Therefore, when pluronic F108 which has a lower molecular weight and owns a longer hydrophilic PEO chain than the PPO chain, increases the hydrophobicity level, thus contributing to drug loading [34]. Niosomes, as they assist to direct the drug to the cancer cells, lengthen the course of treatment with lowered severity of harmful side effects, and enhance drug stability, are a promising drug delivery carrier for cancer therapy [32].

Formulation design

The combined effect of two formulation variables, each at three levels, and the potential nine DTX niosome formulation combinations were investigated using a 32 factorial design. Cholesterol (X1) and Span® 40 (X2) concentrations were the independent variables while the particle size (Y1) and percent EE (Y2) were the dependent variables in this experiment. The optimized batch (F5) showed 97.43% percent EE and a particle size of 244.9 nm.

Effect of formulation variables

The results of the investigations indicate that the response values of the dependent variables vary with the change in independent variables. This is also affected by the spacious area of coefficient values of the polynomial equation terms for Y1. The major outcomes of X1 and X2 describe the typical outcome of increasing one variable from a low to a high level. Out of the 9 formulations, the particle size (Y1) and %EE (Y2) values displayed a large range from 531.6 to 244.9 nm and 66.55 to 97.43%, respectively. It clearly shows that the Y1 and Y2 values were highly influenced by the X1 and X2 variables chosen for the trials. This could also be illustrated by a wide variety of responses for the coefficients of the terms in equations. The primary effects of X1 and X2 indicate the average result of adjusting one variable at a time from low to high. The complete model statistical analysis shown in Table 1 reveals the significant influence of independent variables on the dependent variables.

Table 1 Experimental design of DTX niosomes and effect on dependent variablesAnalysis of variance (ANOVA)

The statistical validity of the polynomials was determined using the Design Expert® software's ANOVA feature. Mathematical designs were created for each response and verified for significance. The adjusted R2 and predicted R2 values were in best recommendations for all responses, showing that the mathematical model accurately predicted the outcomes. The degree of variation in the dependent variable is determined by the independent variables, and it is collectively depicted by polynomial equations. The statistical model generated interactive polynomial terms for each response; the equations are as follows:

$$Y = \beta_ + \beta_ A + \beta_ B + \beta_ AB + \beta_ A^ + \beta_ B^$$

(1)

where Y is the independent variable, β0 represents the arithmetic mean response of the 9 trials, and β1 is the calculated factor of a coefficient. The average outcome when the components were adjusted one at a time from their lower to higher values is represented by the principal effects of the degree of A and B. The interaction terms (AB) demonstrated how the outcomes vary when two variables are altered at the same time. The DoE data suggest that particle size and %EE depend on the selected independent variables. The following polynomial equations were used to derive conclusions based on the statistical sign it bears demonstrating synergistic or antagonistic effects (Table 2).

$$Y_ \left( }\;}} \right) = 244.90 + 24.79A + 1.65B + 70.44AB + 107.86A^ + 91.48B^$$

(2)

$$Y_ \left( \right) = 97.43 + 4.58A + 0.3394B + 3.51AB10.51A^ - 4.75B^$$

(3)

Table 2 ANOVA for a quadratic model of particle size and %EE

The model F-value of 11.36 and 10.80 for particle size and %EE, respectively, indicates its significance. Because of the noise, there is only a 3.64% chance that such high F-values will occur. The P-values for the model were < 0.05. The terms AB, A2, and B2 are important model variables in this scenario. The fit statistics values for the model are presented in Table 3. Adequate precision tests the ratio of a signal to noise. A ratio of more than 4 would be desirable. Thus, the ratio 11.054 shows a suitable signal. The space of the design may be navigated using this model. A coefficient of variation (CV) for a single variable describes the dispersion of the variable. The lower CV describes the smaller residuals relative to the predicted value suggesting a good model fit.

Table 3 Fit statistics for particle size and %EECounter plot and 3D surface plot analysis

The data are presented as 2D contour plots and 3D response surface plots for understanding interactions between the components and their impacts on the responses. These plots are useful in predicting the impact of two factors on the same set of results at the same time. The effects of change in concentrations of independent variables on particle size and %EE are described in the following section.

Effect on particle size

Cholesterol is an essential parameter in the fabrication of niosomal vesicles. Altering the concentration of cholesterol and Span® 40 can alter the particle size. The counterplot presented in Fig. 1A represents the design space for employing independent variables based on particle size.

Fig. 1figure 1

A Counter plot and; B 3D surface response plot for particle size

Effect on percent entrapment efficiency

At the initial phase of experiments, where the concentration of Span® 40 increases the %EE also increases. However, additional increases in the Span® 40 concentration led to a decrease in DTX %EE. Amongst independent variables investigated, %EE was affected mainly due to cholesterol concentration, Fig. 2. The %EE was found to be 97.43 ± 1.2 in the F5 batch hence it was considered as an optimized batch [30].

Fig. 2figure 2

A Counter plot and, B 3D surface response plot %EE of DTX niosomes

Particle size analysis and polydispersity index

The particle size of the optimized DTX niosomal formulation (F5) was found to be 244.9 nm. Figure 3 PDI is a representation of the distribution of particle size within a given sample. The particle size distribution ranged from 244.9 to 531.6 nm. The numerical value of PDI was 0.75 indicating monodisperse samples.

Fig. 3figure 3

The particle size of DTX co-loaded niosomes

Zeta potential

The zeta potential distribution graph of DTX-loaded niosomes is presented in Fig. 4. Niosomes were negatively charged with a zeta potential of about − 10 mV [37].

Fig. 4figure 4

Zeta potential of optimized DTX niosomal formulation (F5)

Transmission electron microscopy

The TEM images of optimized DTX-loaded niosomes formulations (F5) are exhibited in Fig. 5. Niosomes appeared as well-defined spheres with a distinct wall enclosing an aqueous core [38]. Furthermore, the mean niosome size determined by TEM agreed well with that determined by particle size and PDI experiments.

Fig. 5figure 5

TEM image of optimized DTX niosomal formulation (F5)

Fourier transform infrared spectroscopy

The FTIR spectra of DTX and DTX-loaded niosomal formulation were recorded in the range of 4000–400 cm−1. In DTX FTIR spectra, the peaks at 3010 cm−1 and 1502 cm−1 are the characteristic peaks of the benzene ring and 1699 cm−1 indicates the presence of (C=O) carbonyl group in the DTX. The FTIR spectrum of optimized niosomal formulation (F5) is presented in Fig. 6.

Fig. 6figure 6

FTIR overlay spectra of (A) plain DTX (B) optimized DTX niosomal formulation (F5)

Differential scanning calorimetry

The melting point temperature of DTX and DTX niosomal formulation were recorded using DSC (SDT Q600 V20.9 Build 20). The DTX DSC thermogram showed a high endothermic peak at 178-188 ºC that corresponds to the DTX melting. DSC thermograms of DTX-loaded niosomal dispersion interestingly displayed endotherm at 90.46 and 103 °C corresponding to Span® 40, and cholesterol (Fig. 7).

Fig. 7figure 7

DSC of (A) plain DTX; and (B) DTX niosomal formulation (F5)

X-ray diffractometry

Figure 8A and B showed the P-XRD pattern of plain DTX and optimized niosomal formulation batch (F5), respectively. The results obtained from the P-XRD study were in agreement with DSC studies. DTX is a white crystalline powder with multiple distinct peaks at varied relative intensities when viewed via a diffraction angle which disappeared in the P-XRD of the DTX niosomal formulation (F5) [30].

Fig. 8figure 8

P-XRD of (A) plain DTX; and (B) DTX optimized niosomal formulation (F5)

In vitro drug release study

The in vitro drug release from plain DTX and DTX niosomes is presented in Fig. 9. The highest percentage of cumulative drug released in solution from the plain DTX was 85% after 8 h and from DTX co-loaded niosome was 35.2% after 24 h [39].

Fig. 9figure 9

Graphical presentation of cumulative drug release study

In vitro cytotoxicity study

The in vitro cytotoxicity of plain DTX and optimized DTX niosomal formulation (F5) was evaluated in the MCF-7 cells using the MTT assay. The findings of the study are reported as half-maximal inhibitory concentration (IC50). The doses of DTX ranged from 0.0001 to 1 µM. In vitro cytotoxicity of DTX was strongly influenced by drug concentration. In the cell line study, both plain DTX and DTX niosomes successfully inhibited the proliferation in a concentration-dependent manner. IC50 of MCF-7 cells were more responsive to DTX niosomes, as compared to IC50 of plain DTX indicating sustained release of DTX from niosomes over a prolonged time (Fig. 10).

Fig. 10figure 10

Cytotoxicity study plain DTX and optimized DTX niosomal formulation (F5)

Stability study

The stability of niosomal formulations was investigated by keeping track of changes in the physical appearances and %EE by storing them at a temperature of 2–8 °C for 3 months. At the end of the study, there was no significant change in the appearance, particle size, PDI, and zeta potential of the niosomal formulations indicating their better stability. Additionally, DTX content retained throughout 1, 2, and 3 months was 97.20 ± 0.9, 97.13 ± 1.1, and 97.02 ± 0.7%, respectively, against the initial amount of 97.43 ± 1.2%. The plain DTX retained only 80% 0.25 ± 0.3% of its initial 98.52 ± 0.9% indicating niosomes superior stability over plain DTX.

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